The ordinary understanding and ascription of mental states is a multiply complex subject. Widely discussed approaches to the subject, such as functionalism and the theory-theory (TT), have many variations and interpretations. No surprise, then, that there are misunderstandings and disagreements, which place many items on the agenda. Unfortunately, the multiplicity of issues raised by the commentators and the limitations of space make it impossible to give a full reply to everyone. My response is divided into five topics: (1) Which version(s) (...) of functionalism are candidates for an account of folk psychology, and is any such version satisfactory? (2) Where does TT stand in light of the two target articles and commentaries? (3) Does a phenomenological story provide a promising account of mental states, and is there special first-person authority about them? (4) What are suitable methodological assumptions for our subject matter? (5) How well does the simulation theory fare as an account of third-person mentalistic ascription? (shrink)

The comment addresses central issues of a "theory theory" approach as exemplified in Gopnik' and Goldman's BBS-articles. Gopnik, on the one hand, tries to demonstrate that empirical evidence from developmental psychology supports the view of a "theory theory" in which common sense beliefs are constructed to explain ourselves and others. Focusing the informational processing routes possibly involved we would like to argue that his main thesis (e.g. idea of intentionality as a cognitive construct) lacks support at least for two reasons: (...) one methodological and one structural. On the other hand, Goldman raises an important question when he is asking how people ascribe mental states to themselves. Reasons why Goldman's attempt to understand common sense mental representations by using an analogy from visual perception is problematic are discussed. The role which is attributed to a phenomenology is evaluated. (shrink)

This article describes two uses of folk psychology in scientific psychology. Use 1 deals with the way in which folk theories and beliefs are imported into social psychological models on the basis that they exert causal influences on cognition or behavior (regardless of their validity or scientific usefulness). Use 2 describes the practice of mining elements from folk psychology for building an overarching psychological theory that goes beyond common sense (and assumes such elements are valid or scientifically useful). This distinction (...) is then applied to both common practices within psychology and the philosophical arguments concerning the scientific validity of folk psychology. Adopting a social psychological perspective, I argue that (a) the two uses are often conflated in psychology with deleterious consequences; and (b) that the arguments for the elimination of folk psychology as a basis for scientific psychology presented by Churchland and others, are weakened by the failure to attend to this distinction. (shrink)

The central mission of cognitive science is to reveal the real nature of the mind, however familiar or foreign that nature may be to naive preconceptions. The existence of naive conceptions is also important, however. Prescientific thought and language contain concepts of the mental, and these concepts deserve attention from cognitive science. Just as scientific psychology studies folk physics (McCloskey 1983, Hayes 1985), viz., the common understanding (or misunderstanding) of physical phenomena, so it must study folk psychology, the common understanding (...) of mental states. This subfield of scientific psychology is what I mean by the phrase 'the psychology of folk psychology'. (shrink)

Since the early days of artificial intelligence (AI), many logics have been explored as tools for knowledge representation and reasoning. In the spirit of the Crossley Festscrift and recognizing John Crossley’s diverse interests and his legacy in both mathematical logic and computer science, I discuss examples from my own research that sit in the overlap of logic and AI, with a focus on supporting human–AI interactions.

Cognitive Science, Volume 46, Issue 5, May 2022.

We re-evaluate existing data that demonstrate a large amount of variability in the content of categories considering the fact that these data have been obtained in a specific task: the production of features of single isolated categories. We present new data that reveal a large consensus when participants have to judge whether or not a given feature is characteristic of a category and we show that classification tasks produce an intermediate level of consensus. We argue that the differences observed between (...) tasks are due to the extent of implied context and we propose a reinterpretation of typicality effects, demonstrating that they are compatible with the existence of a stable conceptual core. In order to explain how the existence of a conceptual core is consistent with variability due to context, we present a theory of categorisation based on a property tree organisation. Within a domain of description, we distinguish between semantic implications (flying → moving) and empirical implications (flying → having wings) as well as between properties used to describe objects. Semantic implications serve to build property lines and Galois lattices are used to reveal category structures according to empirical implications. We show that variability in the category content may be explained by the fact that some properties are emphasised while others are masked according to the context. (shrink)